Theses and Dissertations

ORCID

https://orcid.org/0000-0003-2698-5664

Advisor

Siegert,Courtney

Committee Member

Himes, Austin

Committee Member

Poudel, Krishna

Committee Member

Renninger, Heidi

Date of Degree

12-12-2025

Original embargo terms

Immediate Worldwide Access

Document Type

Dissertation - Open Access

Major

Forest Resources (Forestry)

Degree Name

Doctor of Philosophy (Ph.D.)

College

College of Forest Resources

Department

Department of Forestry

Abstract

Afforestation is increasingly promoted as a nature-based solution to climate change, although its net impact on soil greenhouse gas (GHG) emissions remains variable across forest types, prior land uses, and environmental conditions. This dissertation investigates the soil GHG flux (CO2, CH4, N2O) responses and dynamics of afforestation under the Conservation Reserve Program (CRP) in the southeastern United States, with a specific focus on broadleaf and conifer tree planting practices. Drawing from a combination of meta-analysis, machine learning, and field-based observations across 73 sites, this research provides an integrated assessment of afforestation’s climate mitigation potential. In Chapter II, a meta-analysis of 52 peer-reviewed studies revealed that afforestation generally reduces or has no impact on soil GHG emissions compared to non-forested lands. Total CO₂-equivalent emissions declined most significantly when cropland, grassland, or peatland were converted to forest, particularly when conifer species were planted. While CH₄ emissions tended to decrease across conversions from all prior land use types, N₂O responses were more variable. Chapter III presents a machine learning analysis of soil CO₂, CH₄, and N₂O fluxes across broadleaf and conifer sites. Random Forest, Extreme Gradient Boosting (XGBoost, a decision-tree–based ensemble machine learning method), together with SHAP (SHapley Additive exPlanations, an approach for interpreting feature contributions), identified soil temperature, air humidity, and water-filled pore space in soils as key predictors of CO₂ and CH₄ fluxes, while N₂O was weakly influenced by soil C:N ratio, clay content and pH. Chapter IV compares soil nitrogen transformation rates, including mineralization and nitrification rate between conifer and broadleaf forest types. Broadleaf forests exhibited higher soil carbon and nitrogen than conifer forest soils. Nitrification rates were significantly lower in broadleaf systems. Forest structure (e.g., vegetation cover, tree height, basal area) and soil properties (e.g., C: N ratio, pH) were found to drive differences in nitrogen transformation. These findings demonstrate that CRP afforestation efforts can provide net climate benefits, particularly when tree species and site conditions are matched to minimize soil GHG emissions. This work highlights the importance of integrating forest type, environmental drivers, and soil biogeochemistry in afforestation planning and climate mitigation policy.

Sponsorship (Optional)

Forest and Wildlife Research Center, Mississippi State University; The United States Department of Agriculture (USDA) Farm Services Agency

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